Due to the representation of the continuous time series, there is no
explicit machinery for addressing discrete events. Consider for
example, a binary switching event such as the output of a
classifier. This could be the output of a speech detection system
which determines if the user speaking or not-speaking. In a time
series, such a phenomenon will typically be represented as a step
function. In the ARL, this could be potentially harmful due to the
projection on the eigenspace. The onset of a step function is a highly
non-linear and localized transition and eigenspace projection will
unnaturally smooth it into a sigmoid like curve. Thus, one would
require an alternative way of representing sudden changes which might
involve fundamental changes to the time series representation.